Abstract »Recent international commitments for achieving carbon neutrality in the present century pave the way for further advancements in wind energy. To satisfy energy demands, potential energy outputs can be enhanced by erections of ever larger wind turbines (WTs). The use of novel composite materials is required for these developments in order to reduce the mass of WT blades (WTBs). However, long-term safety and reliability of WTs are affected by higher flexibilities and lower buckling capacities of these WTBs. Furthermore, current international standards and guidelines recommend rigidly defined physical inspections for examining the structural state of WTs, which significantly contribute to operation and maintenance costs. The development of effective structural health monitoring (SHM) systems for WTs can counteract cost increase by reducing the risk of dramatic failures, scheduling maintenance actions according to the actual state and lowering overall inspection efforts.
The present paper shows a novel approach for damage severity estimation in WTBs based on vibrational responses. First, acceleration signals of a WTB in the healthy and reference damage states are acquired. Second, initial damage sensitive features (DSFs) are obtained from autocorrelation coefficient estimates of these signals. In order to reduce DSF vector dimensions while retaining discriminatory information, the Fukunaga-Koontz transform (FKT) is applied in the third step. The FKT is an extension of the Karhunen-Loeve expansion and provides a ranking of the resulting transformation vectors with respect to the discriminatory information. This information is then used for sequentially constructing a hierarchical adaptive neuro-fuzzy inference system. Using the ranking, inputs, i.e. the scores from transformed initial DSFs, and hierarchy levels are added sequentially until the desired level of accuracy for estimating the damage severity is achieved.
Physical experiments with a small scale WTB are performed in the laboratory. A household fan is used to create a contact-free excitation. Different damage scenarios are simulated non-destructively by attaching small masses, which represent the damage severity in the present study. The results are promising for prospective developments of effective vibration-based SHM systems delivering improved safety and reliability of WTs at lower costs.

Authors

Biography:Simon received his B.Sc. and M.Sc. in civil engineering from the Bauhaus-University Weimar, Germany in 2009 and 2012, respectively. During his Master studies, he got firstly in touch with problems of dynamic testing and structural health monitoring (SHM). He later stayed in Germany as research assistant at the Institute of Structural Mechanics, Weimar. The conducted research project involved advanced numerical simulations as well as the design and installation of monitoring systems for SHM. In 2013, Simon joined the University of Aberdeen’s Lloyd’s Register Foundation Centre for Safety and Reliability Engineering as PhD student. His current work combines time series analysis and machine learning for the application in SHM systems of structural components of wind turbines.

Biography:Piotr graduated with a ME from Technical University of Gdansk, Poland. He then obtained his PhD from The University of Tokyo, Japan for a study on aerodynamics of long span bridges. Piotr later stayed on in Asia working as a postdoctoral researcher at The University of Tokyo and Nanyang Technological University, Singapore. It was then that he developed strong interest in the area of dynamic testing and structural health monitoring which remain the main thrusts of his research activities. In 2003, he became a lecturer at The University of Auckland, New Zealand, and in 2013 moved to The University of Aberdeen’s Lloyd’s Register Foundation Centre for Safety and Reliability Engineering.